Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

TL;DR

This paper provides a comprehensive overview of the current state, challenges, and future opportunities in Graph Neural Networks designed for temporal graphs, highlighting recent advances and open research directions.

Contribution

It offers the first detailed formalization, taxonomy, and analysis of temporal GNN models, addressing a gap in the existing literature.

Findings

Introduces a formal framework for temporal GNNs

Develops a taxonomy based on temporal data processing methods

Identifies key open challenges in the field

Abstract

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current state-of-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives.